Kim, Su Yeon
(IMBdx Inc., Seoul, South Korea)
,
Jeong, Seongmun
(IMBdx Inc., Seoul, South Korea)
,
Lee, Wookjae
(IMBdx Inc., Seoul, South Korea)
,
Jeon, Yujin
(IMBdx Inc., Seoul, South Korea)
,
Kim, Yong-Jin
(IMBdx Inc., Seoul, South Korea)
,
Park, Seowoo
(IMBdx Inc., Seoul, South Korea)
,
Go, Dayoung
(IMBdx Inc., Seoul, South Korea)
,
Lee, Sanghoo
(Seoul Clinical Laboratories Healthcare Inc., Seoul, South Korea)
,
Woo, Hyun Goo
(Ajou University School of Medicine, Suwon, South Korea)
,
Yoon, Jung-Ki
(Stanford Hospital & Clinics, Stanford, CA)
,
Park, Young Sik
(Seoul National University Hospital, Seoul, South Korea)
,
Kim, Young Tae
(Seoul National University Hospital, Seoul, South Korea)
,
Lee, Se-Hoon
(Department of Internal Medicine, Division of Hematology-Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea)
,
Kim, Kwang Hyun
(Ewha Womans University Seoul Hospital, Seoul, South Korea)
,
Lim, Yoojoo
(IMBdx Inc., Seoul, South Korea)
,
Song, Sang-Hyun
(Seoul National University, Seoul, South Korea)
,
Kim, Jin-Soo
(Seoul National University Boramae Medical Center, Seoul, Korea, Republic of (South))
,
Kim, Hwang-Phill
(IMBdx Inc., Seoul, South Korea)
,
Bang, Duhee
(Yonsei University, Seoul, South Korea)
,
Kim, Tae-You
(Seoul National University Hospita)
e15040 Background: The use of cfDNA sequencing has demonstrated great potential for cancer screening; particularly, methylation signatures, copy number variations, and fragmentomic profiles have proven to be effective for identifying early cancer signals. However, most large-scale studies have onl...
e15040 Background: The use of cfDNA sequencing has demonstrated great potential for cancer screening; particularly, methylation signatures, copy number variations, and fragmentomic profiles have proven to be effective for identifying early cancer signals. However, most large-scale studies have only focused on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform for multi-cancer early detection that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Methods: For a total of 950 plasma samples (361 healthy controls and 107 colon, 113 liver, 238 lung, and 131 prostate cancer) and 239 tissue samples, whole-genome methylation sequencing data were generated. Machine learning was conducted for multiple feature types engineered from cfDNA samples, and independent test performance was assessed. Results: A multi-feature cancer signature ensemble (CSE) classifier, integrating all features, outperformed single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models were 77.2% [CI 72-82], 61.4% [CI 56-67], and 60.5% [CI 55-66], respectively; but it was significantly increased to 87.7% [CI 84-91] with CSE ( p-value < 0.0001). For tracing the tissue of origin, CSE enhanced the accuracy beyond the methylation classifier, from 74.7% [CI 69-80] to 77.5% [CI 72-82]. Conclusions: This work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.
e15040 Background: The use of cfDNA sequencing has demonstrated great potential for cancer screening; particularly, methylation signatures, copy number variations, and fragmentomic profiles have proven to be effective for identifying early cancer signals. However, most large-scale studies have only focused on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform for multi-cancer early detection that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Methods: For a total of 950 plasma samples (361 healthy controls and 107 colon, 113 liver, 238 lung, and 131 prostate cancer) and 239 tissue samples, whole-genome methylation sequencing data were generated. Machine learning was conducted for multiple feature types engineered from cfDNA samples, and independent test performance was assessed. Results: A multi-feature cancer signature ensemble (CSE) classifier, integrating all features, outperformed single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models were 77.2% [CI 72-82], 61.4% [CI 56-67], and 60.5% [CI 55-66], respectively; but it was significantly increased to 87.7% [CI 84-91] with CSE ( p-value < 0.0001). For tracing the tissue of origin, CSE enhanced the accuracy beyond the methylation classifier, from 74.7% [CI 69-80] to 77.5% [CI 72-82]. Conclusions: This work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.
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